Transitioning from fossil fuels to renewable energy sources is a critical global challenge; it
demands advances—at the materials, devices and systems levels—for the efficient …

Impurity doping is a promising method to impart new properties to various materials. Due to
their unique optical, magnetic, and electrical properties, rare-earth ions have been …

Deep learning (DL) is one of the fastest-growing topics in materials data science, with
rapidly emerging applications spanning atomistic, image-based, spectral, and textual data …

We provide an introduction to Gaussian process regression (GPR) machine-learning
methods in computational materials science and chemistry. The focus of the present review …

Alloy modelling has a history of machine-learning-like approaches, preceding the tide of
data-science-inspired work. The dawn of computational databases has made the integration …

High-entropy alloys (HEAs) have attracted extensive interest due to their exceptional
mechanical properties and the vast compositional space for new HEAs. However …

New tools enable new ways of working, and materials science is no exception. In materials
discovery, traditional manual, serial, and human-intensive work is being augmented by …

Polarons are quasiparticles that easily form in polarizable materials due to the coupling of
excess electrons or holes with ionic vibrations. These quasiparticles manifest themselves in …

Concrete, as the most widely used construction material, is inextricably connected with
human development. Despite conceptual and methodological progress in concrete science …

Research in the field of high-entropy alloys has been surging since the 2010s. As widely
acknowledged, research interests in this field are largely sparked by the enormous …